A new density-based clustering algorithm, RNN-DBSCAN, is presented which uses reverse
nearest neighbor counts as an estimate of observation density. Clustering is performed …

Data mining is used for finding meaningful information out of a vast expanse of data. With
the advent of Big Data concept, data mining has come to much more prominence …

The DBSCAN method for spatial clustering has received significant attention due to its
applicability in a variety of data analysis tasks. There are fast sequential algorithms for …

Situational awareness tries to grasp the important events and circumstances in the physical
world through sensing, communication, and reasoning. Tracking the evolution of changing …

We present NG-DBSCAN, an approximate density-based clustering algorithm that operates
on arbitrary data and any symmetric distance measure. The distributed design of our …

Distributed machine learning (ML) was originally introduced to solve a complex ML problem
in a parallel way for more efficient usage of computation resources. In recent years, such …

In most parallel DBSCAN algorithms, neighboring points are assigned to the same data
partition for parallel processing to facilitate calculation of the density of the neighbors. This …

DBSCAN is one of clustering algorithms which can report arbitrarily-shaped clusters and
noises without requiring the number of clusters as a parameter (unlike the other clustering …

Spatiotemporal data mining aims to discover interesting, useful but non-trivial patterns in big
spatial and spatiotemporal data. They are used in various application domains such as …

Dbscan is a density-based clustering algorithm which is well known for its ability to discover
clusters of arbitrary shape as well as to distinguish noise. As it is computationally expensive …